Overview

Dataset statistics

Number of variables13
Number of observations156
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory104.8 B

Variable types

Numeric13

Alerts

Jan is highly correlated with Feb and 10 other fieldsHigh correlation
Feb is highly correlated with Jan and 10 other fieldsHigh correlation
Mar is highly correlated with Jan and 10 other fieldsHigh correlation
Apr is highly correlated with Jan and 10 other fieldsHigh correlation
May is highly correlated with Jan and 10 other fieldsHigh correlation
Jun is highly correlated with Jan and 10 other fieldsHigh correlation
Jul is highly correlated with Jan and 10 other fieldsHigh correlation
Aug is highly correlated with Jan and 10 other fieldsHigh correlation
Sep is highly correlated with Jan and 10 other fieldsHigh correlation
Oct is highly correlated with Jan and 10 other fieldsHigh correlation
Nov is highly correlated with Jan and 10 other fieldsHigh correlation
Dec is highly correlated with Jan and 10 other fieldsHigh correlation
Jan is highly correlated with Feb and 10 other fieldsHigh correlation
Feb is highly correlated with Jan and 10 other fieldsHigh correlation
Mar is highly correlated with Jan and 10 other fieldsHigh correlation
Apr is highly correlated with Jan and 10 other fieldsHigh correlation
May is highly correlated with Jan and 10 other fieldsHigh correlation
Jun is highly correlated with Jan and 10 other fieldsHigh correlation
Jul is highly correlated with Jan and 10 other fieldsHigh correlation
Aug is highly correlated with Jan and 10 other fieldsHigh correlation
Sep is highly correlated with Jan and 10 other fieldsHigh correlation
Oct is highly correlated with Jan and 10 other fieldsHigh correlation
Nov is highly correlated with Jan and 10 other fieldsHigh correlation
Dec is highly correlated with Jan and 10 other fieldsHigh correlation
Jan is highly correlated with Feb and 10 other fieldsHigh correlation
Feb is highly correlated with Jan and 10 other fieldsHigh correlation
Mar is highly correlated with Jan and 10 other fieldsHigh correlation
Apr is highly correlated with Jan and 10 other fieldsHigh correlation
May is highly correlated with Jan and 10 other fieldsHigh correlation
Jun is highly correlated with Jan and 10 other fieldsHigh correlation
Jul is highly correlated with Jan and 10 other fieldsHigh correlation
Aug is highly correlated with Jan and 10 other fieldsHigh correlation
Sep is highly correlated with Jan and 10 other fieldsHigh correlation
Oct is highly correlated with Jan and 10 other fieldsHigh correlation
Nov is highly correlated with Jan and 10 other fieldsHigh correlation
Dec is highly correlated with Jan and 10 other fieldsHigh correlation
Year is highly correlated with Jan and 11 other fieldsHigh correlation
Jan is highly correlated with Year and 11 other fieldsHigh correlation
Feb is highly correlated with Year and 11 other fieldsHigh correlation
Mar is highly correlated with Year and 11 other fieldsHigh correlation
Apr is highly correlated with Year and 11 other fieldsHigh correlation
May is highly correlated with Year and 11 other fieldsHigh correlation
Jun is highly correlated with Year and 11 other fieldsHigh correlation
Jul is highly correlated with Year and 11 other fieldsHigh correlation
Aug is highly correlated with Year and 11 other fieldsHigh correlation
Sep is highly correlated with Year and 11 other fieldsHigh correlation
Oct is highly correlated with Year and 11 other fieldsHigh correlation
Nov is highly correlated with Year and 11 other fieldsHigh correlation
Dec is highly correlated with Year and 11 other fieldsHigh correlation
Year is uniformly distributed Uniform
Year has unique values Unique

Reproduction

Analysis started2022-05-17 18:49:34.172741
Analysis finished2022-05-17 18:50:41.846413
Duration1 minute and 7.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1938.5
Minimum1861
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-05-17T11:50:42.260050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile1868.75
Q11899.75
median1938.5
Q31977.25
95-th percentile2008.25
Maximum2016
Range155
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation45.17742799
Coefficient of variation (CV)0.02330535362
Kurtosis-1.2
Mean1938.5
Median Absolute Deviation (MAD)39
Skewness0
Sum302406
Variance2041
MonotonicityStrictly increasing
2022-05-17T11:50:42.674080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18611
 
0.6%
19681
 
0.6%
19611
 
0.6%
19621
 
0.6%
19631
 
0.6%
19641
 
0.6%
19651
 
0.6%
19661
 
0.6%
19671
 
0.6%
19691
 
0.6%
Other values (146)146
93.6%
ValueCountFrequency (%)
18611
0.6%
18621
0.6%
18631
0.6%
18641
0.6%
18651
0.6%
18661
0.6%
18671
0.6%
18681
0.6%
18691
0.6%
18701
0.6%
ValueCountFrequency (%)
20161
0.6%
20151
0.6%
20141
0.6%
20131
0.6%
20121
0.6%
20111
0.6%
20101
0.6%
20091
0.6%
20081
0.6%
20071
0.6%

Jan
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct123
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.009
Minimum-0.276
Maximum0.169
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:43.150436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.276
5-th percentile-0.22975
Q1-0.14675
median0.034
Q30.1025
95-th percentile0.1535
Maximum0.169
Range0.445
Interquartile range (IQR)0.24925

Descriptive statistics

Standard deviation0.1344078531
Coefficient of variation (CV)-14.9342059
Kurtosis-1.206720886
Mean-0.009
Median Absolute Deviation (MAD)0.09
Skewness-0.5089361116
Sum-1.404
Variance0.01806547097
MonotonicityNot monotonic
2022-05-17T11:50:43.536486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0914
 
2.6%
0.0843
 
1.9%
-0.2183
 
1.9%
0.1113
 
1.9%
0.1352
 
1.3%
-0.1492
 
1.3%
0.0162
 
1.3%
0.0132
 
1.3%
-0.1442
 
1.3%
-0.162
 
1.3%
Other values (113)131
84.0%
ValueCountFrequency (%)
-0.2761
0.6%
-0.271
0.6%
-0.2651
0.6%
-0.2631
0.6%
-0.2561
0.6%
-0.2461
0.6%
-0.2341
0.6%
-0.2321
0.6%
-0.2291
0.6%
-0.2211
0.6%
ValueCountFrequency (%)
0.1691
0.6%
0.1652
1.3%
0.1611
0.6%
0.161
0.6%
0.1591
0.6%
0.1561
0.6%
0.1551
0.6%
0.1531
0.6%
0.1511
0.6%
0.151
0.6%

Feb
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct121
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008865384615
Minimum-0.277
Maximum0.169
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:44.368635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.277
5-th percentile-0.23
Q1-0.14525
median0.034
Q30.10225
95-th percentile0.152
Maximum0.169
Range0.446
Interquartile range (IQR)0.2475

Descriptive statistics

Standard deviation0.1346052334
Coefficient of variation (CV)-15.18323674
Kurtosis-1.207863718
Mean-0.008865384615
Median Absolute Deviation (MAD)0.0905
Skewness-0.509318727
Sum-1.383
Variance0.01811856886
MonotonicityNot monotonic
2022-05-17T11:50:44.725175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1443
 
1.9%
0.1163
 
1.9%
0.0893
 
1.9%
0.1263
 
1.9%
0.1153
 
1.9%
0.0362
 
1.3%
-0.0672
 
1.3%
0.0182
 
1.3%
-0.0052
 
1.3%
0.0582
 
1.3%
Other values (111)131
84.0%
ValueCountFrequency (%)
-0.2771
0.6%
-0.2691
0.6%
-0.2661
0.6%
-0.2621
0.6%
-0.2591
0.6%
-0.2441
0.6%
-0.2321
0.6%
-0.232
1.3%
-0.2221
0.6%
-0.2211
0.6%
ValueCountFrequency (%)
0.1691
0.6%
0.1671
0.6%
0.1651
0.6%
0.1611
0.6%
0.1592
1.3%
0.1581
0.6%
0.1551
0.6%
0.1512
1.3%
0.151
0.6%
0.1491
0.6%

Mar
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct125
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008807692308
Minimum-0.277
Maximum0.171
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:45.090867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.277
5-th percentile-0.22825
Q1-0.14625
median0.034
Q30.102
95-th percentile0.15325
Maximum0.171
Range0.448
Interquartile range (IQR)0.24825

Descriptive statistics

Standard deviation0.1348267761
Coefficient of variation (CV)-15.30784357
Kurtosis-1.212123178
Mean-0.008807692308
Median Absolute Deviation (MAD)0.088
Skewness-0.5078029748
Sum-1.374
Variance0.01817825955
MonotonicityNot monotonic
2022-05-17T11:50:45.522902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1155
 
3.2%
0.0874
 
2.6%
-0.2194
 
2.6%
0.093
 
1.9%
0.0853
 
1.9%
0.1023
 
1.9%
-0.0962
 
1.3%
0.0942
 
1.3%
-0.0072
 
1.3%
-0.1422
 
1.3%
Other values (115)126
80.8%
ValueCountFrequency (%)
-0.2771
0.6%
-0.2691
0.6%
-0.2661
0.6%
-0.2621
0.6%
-0.2611
0.6%
-0.2411
0.6%
-0.2321
0.6%
-0.2291
0.6%
-0.2281
0.6%
-0.2261
0.6%
ValueCountFrequency (%)
0.1711
0.6%
0.1691
0.6%
0.1651
0.6%
0.1611
0.6%
0.1592
1.3%
0.1561
0.6%
0.1541
0.6%
0.1531
0.6%
0.1521
0.6%
0.152
1.3%

Apr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008698717949
Minimum-0.278
Maximum0.171
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:45.930928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.278
5-th percentile-0.2275
Q1-0.1465
median0.033
Q30.1025
95-th percentile0.1545
Maximum0.171
Range0.449
Interquartile range (IQR)0.249

Descriptive statistics

Standard deviation0.1349242282
Coefficient of variation (CV)-15.51081768
Kurtosis-1.211698554
Mean-0.008698717949
Median Absolute Deviation (MAD)0.089
Skewness-0.5082209078
Sum-1.357
Variance0.01820454735
MonotonicityNot monotonic
2022-05-17T11:50:46.279954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0893
 
1.9%
0.0873
 
1.9%
-0.1513
 
1.9%
0.1173
 
1.9%
0.0923
 
1.9%
0.1143
 
1.9%
0.1242
 
1.3%
0.0882
 
1.3%
-0.0092
 
1.3%
-0.1812
 
1.3%
Other values (122)130
83.3%
ValueCountFrequency (%)
-0.2781
0.6%
-0.2721
0.6%
-0.2651
0.6%
-0.2631
0.6%
-0.2621
0.6%
-0.2391
0.6%
-0.2331
0.6%
-0.2291
0.6%
-0.2271
0.6%
-0.2241
0.6%
ValueCountFrequency (%)
0.1711
0.6%
0.1691
0.6%
0.1641
0.6%
0.1611
0.6%
0.1591
0.6%
0.1581
0.6%
0.1571
0.6%
0.1561
0.6%
0.1541
0.6%
0.1521
0.6%

May
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008576923077
Minimum-0.277
Maximum0.171
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:46.661580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.277
5-th percentile-0.22675
Q1-0.14525
median0.0315
Q30.10175
95-th percentile0.15425
Maximum0.171
Range0.448
Interquartile range (IQR)0.247

Descriptive statistics

Standard deviation0.1349903241
Coefficient of variation (CV)-15.73878218
Kurtosis-1.213548182
Mean-0.008576923077
Median Absolute Deviation (MAD)0.0885
Skewness-0.5069466789
Sum-1.338
Variance0.01822238759
MonotonicityNot monotonic
2022-05-17T11:50:46.992932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1144
 
2.6%
-0.213
 
1.9%
0.0893
 
1.9%
0.0863
 
1.9%
0.0723
 
1.9%
0.13
 
1.9%
0.1193
 
1.9%
0.0552
 
1.3%
-0.2132
 
1.3%
-0.1912
 
1.3%
Other values (112)128
82.1%
ValueCountFrequency (%)
-0.2771
0.6%
-0.2751
0.6%
-0.2641
0.6%
-0.2622
1.3%
-0.2381
0.6%
-0.2341
0.6%
-0.2321
0.6%
-0.2251
0.6%
-0.2222
1.3%
-0.2182
1.3%
ValueCountFrequency (%)
0.1711
0.6%
0.1681
0.6%
0.1631
0.6%
0.1612
1.3%
0.1591
0.6%
0.1582
1.3%
0.1531
0.6%
0.1521
0.6%
0.1511
0.6%
0.151
0.6%

Jun
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct129
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008416666667
Minimum-0.275
Maximum0.17
Zeros0
Zeros (%)0.0%
Negative66
Negative (%)42.3%
Memory size1.3 KiB
2022-05-17T11:50:47.359742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.275
5-th percentile-0.2265
Q1-0.1445
median0.0305
Q30.10225
95-th percentile0.1545
Maximum0.17
Range0.445
Interquartile range (IQR)0.24675

Descriptive statistics

Standard deviation0.1349967244
Coefficient of variation (CV)-16.03921478
Kurtosis-1.215001118
Mean-0.008416666667
Median Absolute Deviation (MAD)0.0905
Skewness-0.5067856856
Sum-1.313
Variance0.01822411559
MonotonicityNot monotonic
2022-05-17T11:50:47.705383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1144
 
2.6%
0.0913
 
1.9%
0.1213
 
1.9%
-0.1512
 
1.3%
-0.1982
 
1.3%
0.1392
 
1.3%
-0.2752
 
1.3%
-0.2182
 
1.3%
-0.1892
 
1.3%
0.0292
 
1.3%
Other values (119)132
84.6%
ValueCountFrequency (%)
-0.2752
1.3%
-0.2641
0.6%
-0.2631
0.6%
-0.261
0.6%
-0.2381
0.6%
-0.2351
0.6%
-0.2311
0.6%
-0.2251
0.6%
-0.2241
0.6%
-0.2211
0.6%
ValueCountFrequency (%)
0.171
0.6%
0.1661
0.6%
0.1621
0.6%
0.1612
1.3%
0.161
0.6%
0.1591
0.6%
0.1561
0.6%
0.1541
0.6%
0.1532
1.3%
0.1491
0.6%

Jul
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct128
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008230769231
Minimum-0.276
Maximum0.17
Zeros0
Zeros (%)0.0%
Negative65
Negative (%)41.7%
Memory size1.3 KiB
2022-05-17T11:50:48.101896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.276
5-th percentile-0.22675
Q1-0.1455
median0.033
Q30.10125
95-th percentile0.15475
Maximum0.17
Range0.446
Interquartile range (IQR)0.24675

Descriptive statistics

Standard deviation0.1349345766
Coefficient of variation (CV)-16.39392052
Kurtosis-1.217675813
Mean-0.008230769231
Median Absolute Deviation (MAD)0.0915
Skewness-0.5064557518
Sum-1.284
Variance0.01820733995
MonotonicityNot monotonic
2022-05-17T11:50:48.476924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0843
 
1.9%
0.1543
 
1.9%
-0.1922
 
1.3%
0.1112
 
1.3%
-0.0122
 
1.3%
0.0332
 
1.3%
0.0222
 
1.3%
-0.0872
 
1.3%
0.0172
 
1.3%
0.0272
 
1.3%
Other values (118)134
85.9%
ValueCountFrequency (%)
-0.2761
0.6%
-0.2731
0.6%
-0.2641
0.6%
-0.2621
0.6%
-0.2571
0.6%
-0.241
0.6%
-0.2331
0.6%
-0.2291
0.6%
-0.2261
0.6%
-0.2241
0.6%
ValueCountFrequency (%)
0.171
 
0.6%
0.1661
 
0.6%
0.1621
 
0.6%
0.1612
1.3%
0.1591
 
0.6%
0.1581
 
0.6%
0.1571
 
0.6%
0.1543
1.9%
0.1481
 
0.6%
0.1471
 
0.6%

Aug
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct125
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008102564103
Minimum-0.278
Maximum0.169
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)41.0%
Memory size1.3 KiB
2022-05-17T11:50:48.914961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.278
5-th percentile-0.22675
Q1-0.1455
median0.037
Q30.10325
95-th percentile0.155
Maximum0.169
Range0.447
Interquartile range (IQR)0.24875

Descriptive statistics

Standard deviation0.1350526865
Coefficient of variation (CV)-16.66789485
Kurtosis-1.217238848
Mean-0.008102564103
Median Absolute Deviation (MAD)0.088
Skewness-0.505543938
Sum-1.264
Variance0.01823922812
MonotonicityNot monotonic
2022-05-17T11:50:49.293987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0844
 
2.6%
0.0973
 
1.9%
0.1293
 
1.9%
0.0572
 
1.3%
0.0912
 
1.3%
-0.1952
 
1.3%
-0.1692
 
1.3%
-0.1922
 
1.3%
-0.1452
 
1.3%
-0.1472
 
1.3%
Other values (115)132
84.6%
ValueCountFrequency (%)
-0.2781
0.6%
-0.2721
0.6%
-0.2641
0.6%
-0.2611
0.6%
-0.2551
0.6%
-0.2411
0.6%
-0.2331
0.6%
-0.2291
0.6%
-0.2261
0.6%
-0.2251
0.6%
ValueCountFrequency (%)
0.1691
0.6%
0.1661
0.6%
0.1632
1.3%
0.1621
0.6%
0.1611
0.6%
0.1591
0.6%
0.1552
1.3%
0.1542
1.3%
0.151
0.6%
0.1451
0.6%

Sep
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct129
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008051282051
Minimum-0.278
Maximum0.168
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)41.0%
Memory size1.3 KiB
2022-05-17T11:50:49.688017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.278
5-th percentile-0.228
Q1-0.1445
median0.0385
Q30.1045
95-th percentile0.15425
Maximum0.168
Range0.446
Interquartile range (IQR)0.249

Descriptive statistics

Standard deviation0.1349663659
Coefficient of variation (CV)-16.76333844
Kurtosis-1.216271947
Mean-0.008051282051
Median Absolute Deviation (MAD)0.0865
Skewness-0.5047730202
Sum-1.256
Variance0.01821591993
MonotonicityNot monotonic
2022-05-17T11:50:50.043044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0844
 
2.6%
0.0973
 
1.9%
0.0063
 
1.9%
0.0852
 
1.3%
0.0932
 
1.3%
-0.1872
 
1.3%
-0.1442
 
1.3%
0.1532
 
1.3%
-0.2282
 
1.3%
-0.0142
 
1.3%
Other values (119)132
84.6%
ValueCountFrequency (%)
-0.2781
0.6%
-0.271
0.6%
-0.2641
0.6%
-0.2621
0.6%
-0.2521
0.6%
-0.2421
0.6%
-0.2321
0.6%
-0.2282
1.3%
-0.2261
0.6%
-0.2231
0.6%
ValueCountFrequency (%)
0.1681
0.6%
0.1651
0.6%
0.1642
1.3%
0.1631
0.6%
0.1611
0.6%
0.161
0.6%
0.1551
0.6%
0.1541
0.6%
0.1532
1.3%
0.1521
0.6%

Oct
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008102564103
Minimum-0.278
Maximum0.168
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)41.0%
Memory size1.3 KiB
2022-05-17T11:50:50.529084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.278
5-th percentile-0.22775
Q1-0.14525
median0.039
Q30.1015
95-th percentile0.155
Maximum0.168
Range0.446
Interquartile range (IQR)0.24675

Descriptive statistics

Standard deviation0.1348787355
Coefficient of variation (CV)-16.64642621
Kurtosis-1.211435005
Mean-0.008102564103
Median Absolute Deviation (MAD)0.086
Skewness-0.5048583882
Sum-1.264
Variance0.01819227328
MonotonicityNot monotonic
2022-05-17T11:50:50.944114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0963
 
1.9%
0.1093
 
1.9%
0.1553
 
1.9%
0.042
 
1.3%
0.1192
 
1.3%
-0.0352
 
1.3%
0.0172
 
1.3%
0.1082
 
1.3%
0.1372
 
1.3%
0.1282
 
1.3%
Other values (122)133
85.3%
ValueCountFrequency (%)
-0.2781
0.6%
-0.271
0.6%
-0.2641
0.6%
-0.2631
0.6%
-0.2511
0.6%
-0.2451
0.6%
-0.2331
0.6%
-0.231
0.6%
-0.2271
0.6%
-0.2261
0.6%
ValueCountFrequency (%)
0.1681
 
0.6%
0.1671
 
0.6%
0.1661
 
0.6%
0.1641
 
0.6%
0.1631
 
0.6%
0.1611
 
0.6%
0.1591
 
0.6%
0.1553
1.9%
0.1531
 
0.6%
0.1511
 
0.6%

Nov
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct137
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008320512821
Minimum-0.277
Maximum0.171
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)41.0%
Memory size1.3 KiB
2022-05-17T11:50:51.291143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.277
5-th percentile-0.22825
Q1-0.1455
median0.0355
Q30.1035
95-th percentile0.1555
Maximum0.171
Range0.448
Interquartile range (IQR)0.249

Descriptive statistics

Standard deviation0.1349777514
Coefficient of variation (CV)-16.22228754
Kurtosis-1.211916434
Mean-0.008320512821
Median Absolute Deviation (MAD)0.0895
Skewness-0.5027368114
Sum-1.298
Variance0.01821899338
MonotonicityNot monotonic
2022-05-17T11:50:51.630165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0973
 
1.9%
0.1342
 
1.3%
0.1132
 
1.3%
0.0012
 
1.3%
-0.2162
 
1.3%
0.0892
 
1.3%
0.1162
 
1.3%
0.0962
 
1.3%
0.1362
 
1.3%
0.1182
 
1.3%
Other values (127)135
86.5%
ValueCountFrequency (%)
-0.2771
0.6%
-0.271
0.6%
-0.2641
0.6%
-0.2631
0.6%
-0.251
0.6%
-0.2481
0.6%
-0.2341
0.6%
-0.2321
0.6%
-0.2271
0.6%
-0.2261
0.6%
ValueCountFrequency (%)
0.1711
0.6%
0.1671
0.6%
0.1651
0.6%
0.1641
0.6%
0.1631
0.6%
0.1611
0.6%
0.1572
1.3%
0.1551
0.6%
0.1541
0.6%
0.1521
0.6%

Dec
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct123
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00816025641
Minimum-0.276
Maximum0.174
Zeros1
Zeros (%)0.6%
Negative65
Negative (%)41.7%
Memory size1.3 KiB
2022-05-17T11:50:52.388847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.276
5-th percentile-0.229
Q1-0.14575
median0.035
Q30.1035
95-th percentile0.155
Maximum0.174
Range0.45
Interquartile range (IQR)0.24925

Descriptive statistics

Standard deviation0.1350260427
Coefficient of variation (CV)-16.54678921
Kurtosis-1.210421442
Mean-0.00816025641
Median Absolute Deviation (MAD)0.09
Skewness-0.5037749096
Sum-1.273
Variance0.01823203222
MonotonicityNot monotonic
2022-05-17T11:50:52.719871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0974
 
2.6%
-0.2183
 
1.9%
0.0133
 
1.9%
-0.1853
 
1.9%
0.1552
 
1.3%
-0.1622
 
1.3%
-0.1442
 
1.3%
-0.0222
 
1.3%
-0.0732
 
1.3%
-0.1542
 
1.3%
Other values (113)131
84.0%
ValueCountFrequency (%)
-0.2761
0.6%
-0.271
0.6%
-0.2651
0.6%
-0.2631
0.6%
-0.2521
0.6%
-0.2481
0.6%
-0.2341
0.6%
-0.2321
0.6%
-0.2281
0.6%
-0.2231
0.6%
ValueCountFrequency (%)
0.1741
0.6%
0.1681
0.6%
0.1642
1.3%
0.1631
0.6%
0.1611
0.6%
0.1581
0.6%
0.1552
1.3%
0.1541
0.6%
0.1521
0.6%
0.1511
0.6%

Interactions

2022-05-17T11:50:37.445117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:47.088765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:52.940314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:57.236290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:01.910478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:05.474777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:09.374742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:13.644089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:17.396094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:21.272007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:25.710516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:29.862839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:33.560905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:37.727129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:49.093583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:53.238357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:57.619024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:02.218495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:05.831862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:09.752777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:13.943592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:17.730619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:21.552025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:26.180562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:30.186315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:34.334210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:37.972746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:49.475628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:53.483382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:57.910528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:02.485520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:06.104124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:10.085801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:14.225613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:17.996650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:21.828045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:26.529409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:30.535088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:34.611236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:38.217769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:49.864657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:53.730998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:58.176549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:02.769070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:06.398436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:10.457830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:14.483641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:18.258704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:22.108046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:26.881474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:30.806102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:34.864141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:38.470798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:50.202895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:53.988603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:58.518573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:03.045100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:06.683649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:10.785850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:14.722683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:18.542726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:22.798059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:27.172502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:31.077130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:35.131162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:38.703813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:50.585047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:54.347438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:58.903017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:03.317112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:06.973386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:11.069871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:15.018612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:18.823748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:23.040591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:27.483411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:31.358154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:35.529193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:38.927834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:50.897072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:54.729995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:59.156620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:03.571556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:07.245585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:11.803540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:15.300641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:19.101435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:23.279606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:27.791956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:31.623183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:35.800214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:39.161866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:51.169091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:55.053016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:59.386158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:03.827462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:07.532748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:12.047847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:15.634673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:19.362459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:23.584656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:28.097357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:31.888043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:36.021232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:39.384879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:51.469551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:55.534056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:00.451290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:04.110628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:07.838766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:12.301816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:16.007443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:19.638047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:23.850488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:28.422674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:32.171388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:36.259249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:39.637901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:51.766829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:55.893089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:00.784832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:04.397016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:08.123788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:12.554970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:16.279458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:20.038306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:24.170514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:28.759145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:32.459451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:36.499267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:39.889918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:52.059494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:56.240102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:01.118381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:04.650062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:08.443088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:12.807512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:16.574725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:20.432938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:24.569001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:29.034257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:32.750486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:36.740292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:40.173940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:52.363124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:56.584126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:01.365396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:04.919941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:08.761693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:13.097070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:16.839745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:20.786967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:24.940028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:29.282278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:33.019528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:36.999450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:40.501964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:52.647287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:49:56.903045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:01.649266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:05.180755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:09.076720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:13.380034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:17.109964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:21.045989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:25.314489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:29.609300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:33.308551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:50:37.223088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-17T11:50:53.031896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-17T11:50:53.593842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-17T11:50:54.031879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-17T11:50:54.473912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-17T11:50:40.961996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-17T11:50:41.540038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

YearJanFebMarAprMayJunJulAugSepOctNovDec
018610.0550.0550.0550.0550.0550.0560.0560.0570.0560.0550.0560.057
118620.0580.0580.0620.0650.0660.0670.0670.0660.0660.0660.0680.068
218630.0710.0740.0780.0810.0810.0820.0860.0890.0910.0890.0870.086
318640.0840.0860.0870.0890.0900.0910.0940.0960.0970.0960.0940.093
418650.0910.0900.0900.0890.0890.0870.0840.0830.0840.0850.0860.087
518660.0840.0830.0850.0870.0860.0850.0840.0810.0780.0740.0720.071
618670.0700.0690.0700.0700.0720.0750.0780.0840.0890.0920.0930.096
718680.0990.1000.1010.1010.1000.1000.1000.1030.1070.1090.1110.114
818690.1150.1160.1180.1180.1190.1190.1190.1170.1150.1130.1120.108
918700.1050.1040.1020.1000.1010.1020.1010.1000.1010.0990.0970.094

Last rows

YearJanFebMarAprMayJunJulAugSepOctNovDec
14620070.1530.1510.1500.1490.1510.1530.1570.1620.1640.1660.1670.168
14720080.1690.1690.1710.1710.1710.1700.1700.1690.1680.1670.1650.163
14820090.1610.1590.1560.1540.1530.1540.1540.1550.1550.1550.1540.152
14920100.1510.1500.1480.1450.1430.1410.1390.1370.1350.1350.1340.135
15020110.1350.1350.1360.1370.1380.1390.1400.1410.1410.1410.1420.142
15120120.1430.1430.1430.1440.1450.1470.1480.1500.1520.1550.1570.158
15220130.1590.1590.1590.1580.1580.1560.1540.1540.1530.1530.1510.151
15320140.1500.1510.1540.1560.1580.1600.1620.1630.1630.1640.1640.164
15420150.1650.1670.1690.1690.1680.1660.1660.1660.1640.1630.1610.161
15520160.1600.1610.1610.1610.1610.1610.1610.1630.1650.1680.1710.174